MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 12 lectures (1h 41m) | Size: 565.7 MB
A clear understanding about the machine learning theory, techniques and its application in Jupyter Notebook platform.
A clear understanding about the machine learning theory, techniques and its application in Jupyter Notebook platform
The program builds a solid foundation by covering the most popular and widely used machine learning technologies and its applications.
Course includes Naive Bayes theory, K Nearest Neighbors (KNN) theory and application, Random forest theory and application, Gradient Boosting Theory etc.
Students will have a good working knowledge on Machine Learning on Python
They can use it for their Educational as well as Business projects and assignments.
A knowledge of analytical techniques and their applications on Python
There are people who are eager to move to Analytics careers but do not have the requisite skill sets. As we move into our 12th year in the Analytics Industry, OrangeTree Global has designed specific courses for freshers and working professionals who are looking at moving to Data Science, Machine Learning and Big Data Careers.
Since 2009, OrangeTree Global has embarked on an ambitious vision of providing affordable and effective Analytics Training and Education across the country.
OrangeTree Global has over a decade’s experience in upskilling professionals and helping them move to analytics jobs and careers within and outside India. If you are reading this, we hope to be a part of your journey too.The program builds a solid foundation by covering the most popular and widely used machine learning technologies and its applications, including Naive Bayes theory and application, K Nearest Neighbors (KNN) theory and application, Random forest theory and application, Gradient Boosting Theory and Application and also Support Vector Machine Theory and Application-laying the building blocks for truly expanded analytical abilities.
The program builds a solid foundation by covering the most popular and widely used machine learning technologies and its applications, including Naive Bayes theory and application, K Nearest Neighbors (KNN) theory and application, Random forest theory and application, Gradient Boosting Theory and Application and also Support Vector Machine Theory and Application-laying the building blocks for truly expanded analytical abilities.
Students and working professionals
DOWNLOAD
uploadgig.com
nitro.download
